This article presents the evolution of the first fully automated simulation based mask defect dispositioning and defect management system used since late 2002 in a production environment at Intel Mask Operation (IMO). Given that inspection tools flag defects which may or may not have any lithographic significance, it makes sense to repair only those defects that resolve on the wafer. The system described here is a fully automated defect dispositioning system, where the lithographic impact of a defect is determined through computer simulation of the mask level image. From the simulated aerial images, combined with image processing techniques, the system can automatically determine the actual critical dimension (CD) impact (in nanometers). Then, using the product specification as a criteria, can pass or fail the defect. Furthermore, this system allows engineers and technicians in the factory to track defects as they are repaired, compare defects at various inspection steps and annotate repair history. Trends such as yield and defect commonality can also be determined. The article concludes with performance results, indicating the speed and accuracy of the system, as well as the savings in the number of defects needing repair.

DIVAS: Fully automated simulation based Mask defect dispositioning and defect management system

Reducing defect repairs and accelerating defect analysis is becoming more important as the total cost of defect repairs 
on advanced masks increases. Photomask defect specs based on printability, as measured on AIMS microscopes has 
been used for years, but the fundamental defect spec is still the defect size, as measured on the photomask, requiring the 
repair of many unprintable defects. 
ADAS, the Automated Defect Analysis System from AVI is now available in most advanced mask shops. It makes the 
use of pure printability specs, or "Optimal Defect Specs" practical. This software uses advanced algorithms to eliminate 
false defects caused by approximations in the inspection algorithm, classify each defect, simulate each defect and 
disposition each defect based on its printability and location. 
This paper defines "optimal defect specs", explains why they are now practical and economic, gives a method of 
determining them and provides accuracy data.

Defining defect specifications to optimize photomask production and requalification

A new method for extracting a photomask defect by using two optical photomask images, a reference image and a test image, is described in this paper. If the same scale, the same height-to-width ratio, zero parallel translation, and zero rotation between two images are assumed, then a large pixel value of the absolute difference image between the images would indicate the existence of a defect at the pixel. However, not only a true defect but a false defect also causes some large pixel values in the absolute difference image due to illumination difference between the reference image and the test image. To adjust the illumination of the reference image to that of the test image, a dynamic programming is used, and the defect in the test image is segmented.

Photomask Defect Extraction by Using Difference between a Reference Image and a Test Image after Illumination Adjustment

ADAS (Automated Defect Analysis Software) is the first product to fully automate mask defect analysis for mask shops
and fabs. ADAS classifies and dispositions photomask defects quickly and accurately. Disposition is based on defect
size and printability measurements from simulation.
Full analysis of inspection reports with 100 defects requires 2 seconds. Printability measurements match AIMS within 6
percent at 3 sigma on 45 nm test masks. Repeatability is 5 percent at 3 sigma over multiple inspections. ADAS can
reduce the need for production AIMS measurements by 90% and eliminate operator review errors and the repelliclizations
they cause. ADAS increases overall inspection efficiency for mask shop first-inspection and final
inspection. It can automate fab requalification inspections and eliminate the need for incoming inspection.

Automating defect disposition in fabs and maskshops

Ever imagine life without a search engine? Now imagine if there was a search engine for reticle defects that allowed
you to search, trend size, and relate defects on any reticle from any inspection tool, in any of your interconnected
fabs, at any time with a performance similar to a typical Google search. What would you do with it? With EUV
upon us and the uncertainly involving EUV defect classification, it is important that the process engineers have
access to as much defect data as possible. Here such data is made available on Windows based PCs and tablet
computers.

RDMS: a Windows and tablet PC based reticle defect searchdatabase with AHDC for interconnected mask and wafer fabs

The detection, classification and disposition of defects is an important function that commands significant resources in mask making. Current processes use manual evaluation of defects, which is slow, subject to errors, and provides sparse data for process improvement. The automated defect analysis software described here reads inspection reports from mask inspection tools, classifies each defect, and measures both its size and printability. It combines and compares data from multiple inspections to provide critical process development data. Data from 144 masks is presented showing that the system missed no critical defects found by operators. These inspections also demonstrated numerous occasions for improved classifications compared to that given by the operators. This capability gives improved disposition, an easy path to using simulator based printability for disposition, and significant improvements in mask yield.

Peter Fiekowsky

Papers

An automated mask defect analysis system for increasing mask shop productivity

An automated mask defect analysis system for increasing mask shop productivity